Latin-Hypercube Design Over Bounded Parameters

Generates a Latin-hypercube sample of size n over the parameter bounds in bounds. Each column is a random permutation of stratified uniform draws (one per equal-width bin in (0, 1]), then scaled to the supplied parameter range. The result is reproducible when seed is supplied.

Usage

lhs_design(n, bounds, seed = NULL)

Arguments

n: Integer. Number of design points (rows). Must be >= 2.
bounds: Two-column numeric matrix or data.frame of [lower, upper] bounds, with one row per parameter. If named, row names propagate to the column names of the returned design.
seed: Integer or NULL. Random seed for reproducibility.

Value

A numeric matrix of dimension n x nrow(bounds). Column names are inherited from rownames(bounds) when available, otherwise theta1, theta2, ....

Details

This is a lightweight helper aimed at pre-PESTO screening: produce a design matrix to feed an APSIM (or any) simulator, then pass the resulting input/output pairs to hsic_identifiability() to flag unidentifiable parameters before ensemble-smoother calibration.

References

McKay, M. D., Beckman, R. J., & Conover, W. J. (1979). A comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics, 21(2), 239-245.

Examples

bounds <- rbind(
  slope     = c(0.1, 2.0),
  intercept = c(-1, 1),
  noise_sd  = c(0.05, 0.5)
)
design <- lhs_design(50, bounds, seed = 1)
head(design)
#>          slope  intercept   noise_sd
#> [1,] 0.2388260  0.5559651 0.18341284
#> [2,] 1.5693166  0.1229079 0.06067908
#> [3,] 0.1198987  0.3486707 0.05838398
#> [4,] 1.3580965 -0.6236229 0.07339595
#> [5,] 0.9411551 -0.9644144 0.12972970
#> [6,] 1.7191804  0.6863797 0.39026021